SaaS plan users are billed essentially the on-demand pricing of the chosen cloud provider, billed per-second, which varies by provider and region. You can follow this on the Valohai web app.

But you to pay less than you would normally because Valohai only bills you when your code has started running on the machine (in addition to the time it takes to download the Docker image and inputs). We don’t bill for the instance boot time, if there is any, and of course, we shut down the servers automatically for you so they don’t hang around.

Private installation users (Enterprise plan) are not billed extra of the resources they use as they already get billed by the respective cloud provider where we setup the installation.

If you are part of our non-free level plans, you get billed on monthly basis according to https://valohai.com/pricing/ or separately signed Enterprise contract.

Not necessarily, Valohai command-line client allows creating one-off executions from local files.
These ad-hoc executions allow quick iteration with the platform when you are still developing your whole pipeline.

I should be able to ignore venv/, datasets/ or other specific files in the same folder structure as valohai.yaml when using the command-line client. It takes forever to launch adhoc executions!¶

You can! Our command-line client ignores everything that git ignores so just add those to your .gitignore and you are good to go.

There is some redundancy between the command-line arguments defined on the train.py , on the valohai.yaml and the command-line client.¶

Yup, we’ve noticed the same nuisance with redundant definitions in e.g. Python argparse definitions and what we define in the YAML file. As we support essentially any programming language or arbitrary command-line tool already installed on the Docker images it is hard to remove this redundancy, unfortunately.

Everything written to STDOUT should be white, and everything written to STDERR should be yellow. So if you see yellow text, then some library is writing to STDERR. For example, TensorFlow tf.Print used to log to STDERR by default.

To fix this, you need to check the relevant framework that is producing the log and see how to make it log to STDOUT.

How can I do so that there are multiple valohai.yaml for different folders in a repo so that I don’t have to split my different models in different repos?¶

For the time being, the easiest way to do this would be defining them all in the same valohai.yaml and just create more steps in there. We have currently no plans to change this behavior as it can get messy fast. We feel it is nicer to have all the Valohai specific configuration in one place.